A dataset of continuous affect annotations and physiological signals for emotion analysis
Abstract
From a computational viewpoint, emotions continue to be intriguingly hard to understand. In research, direct, real-time inspection in realistic settings is not possible. Discrete, indirect, post-hoc recordings are therefore the norm. As a result, proper emotion assessment remains a problematic issue. The Continuously Annotated Signals of Emotion (CASE) dataset provides a solution as it focusses on real-time continuous annotation of emotions, as experienced by the participants, while watching various videos. For this purpose, a novel, intuitive joystick-based annotation interface was developed, that allowed for simultaneous reporting of valence and arousal, that are instead often annotated independently. In parallel, eight high quality, synchronized physiological recordings (1000 Hz, 16-bit ADC) were made of ECG, BVP, EMG (3x), GSR (or EDA), respiration and skin temperature. The dataset consists of the physiological and annotation data from 30 participants, 15 male and 15 female, who watched several validated video-stimuli. The validity of the emotion induction, as exemplified by the annotation and physiological data, is also presented.
Cite
@article{arxiv.1812.02782,
title = {A dataset of continuous affect annotations and physiological signals for emotion analysis},
author = {Karan Sharma and Claudio Castellini and Egon L. van den Broek and Alin Albu-Schaeffer and Friedhelm Schwenker},
journal= {arXiv preprint arXiv:1812.02782},
year = {2018}
}
Comments
Dataset available at: https://rmc.dlr.de/download/CASE_dataset/CASE_dataset.zip